Can kinematic parameters of 3D reach-to-target movements be used as a proxy for clinical outcome measures in chronic stroke rehabilitation? An exploratory study

The use of home-based digital technology to support post-stroke upper limb rehabilitation: A scoping review

OBJECTIVE

To identify, map and synthesize the extent and nature of existing studies on the use of home-based digital technology to support post-stroke upper limb rehabilitation.

DATA SOURCES

A comprehensive literature search was completed between 30 May 2022 and 05 April 2023, from seven online databases (CINAHL, Cochrane Library, PubMed, ScienceDirect, IEEExplore, Web of Science and PEDro), Google Scholar and the reference lists of already identified articles.

METHODS

A scoping review was conducted according to Arksey and O'Malley (2005), and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. All English-language studies reporting on the use of home-based digital technology to support upper limb post-stroke rehabilitation were eligible for inclusion.

RESULTS

The search generated a total of 1895 records, of which 76 articles met the inclusion criteria. Of these, 52 were experimental studies and the rest, qualitative, case series and case studies. Of the overall 2149 participants, 2028 were stroke survivors with upper limb impairment. The majority of studies were aimed at developing, designing and/or assessing the feasibility, acceptability and efficacy of a digital system for poststroke upper limb rehabilitation in home settings. The thematic analysis found six major categories: Tele-rehabilitation (n = 29), games (n = 45), virtual reality (n = 26), sensor (n = 22), mobile technology (n = 22), and robotics (n = 8).

CONCLUSION

The digital technologies used in post-stroke upper limb rehabilitation were multimodal, and system-based comprising telerehabilitation, gamification, virtual reality, mobile technology, sensors and robotics. Furthermore, future research should focus to determine the effectiveness of these modalities.

Correlation between kinetic and kinematic measures, clinical tests and subjective self-evaluation questionnaires of the affected upper limb in people after stroke

Introduction

Assessment of stroke recovery should include multiple sources of information in order to obtain a complete understanding of the individual's rehabilitation progress. Self-evaluation questionnaires' scores do not always correspond to the scores of commonly used clinical evaluation tools. The purpose of this study was to assess the relationship between self-evaluation questionnaires, clinical tests, and kinematic and kinetic analyses of the affected upper limb after stroke, and to determine the correlation between these measures and self-reported general function 2-4 years after the stroke.

Methods

Twenty-six subjects recovering from stroke were included in the study. Spearman's correlation coefficient was used to measure the correlation between Stroke Impact Scale (SIS), Motor activity Log (MAL), Fugl-Meyer Assessment (FMA) and Action Reach Arm Test (ARAT) scores, and kinematic and kinetic analyses. A logistic regression was used to assess the extent to which these measures may predict the participants' functional self-reported status 2-4 years post stroke.

Results

Sections regarding hand function, hand force and general ADL of the self-evaluation questionnaires correlated with kinematic variables. However, only questionnaires that focus on hand function correlated with clinical tests. Mean and maximal hand velocity had the strongest correlations with self-evaluation questionnaires and with the clinical tests, more than other kinematic variables. Self-evaluation questionnaires and clinical tests were found to be correlated with hand kinetic metrics force-to-time ratio and number of force peaks. SIS hand force domain, mean velocity and maximal velocity predicted self-reported general function 2-4 years after the stroke.

Conclusion

Self-evaluation questionnaires should be considered for wider use in the clinical evaluation of a patient's stroke recovery, since they add important information on the individual's functional status, which is not reflected in the clinical tests.

A Multimodal Analysis to Explore Upper Limb Motor Recovery at 4 Weeks After Stroke: Insights From EEG and Kinematics Measures

Background. Stroke is a leading cause of death and disability worldwide and there is a very short period of increased synaptic plasticity, fundamental in motor recovery. Thus, it is crucial to acquire data to guide the rehabilitation treatment. Promising results have been achieved with kinematics and neurophysiological data, but currently, few studies integrate these different modalities. Objectives. We explored the correlations between standardized clinical scales, kinematic data, and EEG measures 4 weeks after stroke. Methods. 26 patients were considered. Among them, 20 patients also performed the EEG study, beyond the kinematic analysis, at 4 weeks. Results. We found correlations between the Fugl-Meyer Assessment-Upper Extremity, movement duration, smoothness measures, and velocity peaks. Moreover, EEG measures showed a tendency for the healthy hemisphere to vicariate the affected one in patients characterized by better clinical conditions. Conclusions. These results suggest the relevance of kinematic (in particular movement duration and smoothness) and EEG biomarkers to evaluate post-stroke recovery. We emphasize the importance of integrating clinical data with kinematic and EEG analyses from the early stroke stages, in order to guide rehabilitation strategies to best leverage the short period of increased synaptic plasticity.

Making Best Use of Home-Based Rehabilitation Robots

Large-scale clinical trials have shown that rehabilitation robots are as affective as conventional therapy, but the cost-effectiveness is preventing their uptake. This study investigated whether a low-cost rehabilitation robot could be deployed in a home setting for rehabilitation of people recovering from stroke (n = 16) and whether clinical outcome measures correlated well with kinematic measures gathered by the robot. The results support the feasibility of patients independently using the robot with improvement in both clinical measures and kinematic data. We recommend using kinematic data early in an intervention to detect improvement while using a robotic device. The kinematic measures in the assessment task (hits/minute and normalised jerk) adequately pick up changes within a four-week period, thus allowing the rehabilitation regime to be adapted to suit the user’s needs. Estimating the long-term clinical benefit must be explored in future research.